Optimal Sequential Search Among Alternatives

48 Pages Posted: 26 Oct 2016 Last revised: 3 Feb 2020

See all articles by Michael Choi

Michael Choi

University of California, Irvine

Lones Smith

University of Wisconsin at Madison - Department of Economics

Date Written: October 26, 2016

Abstract

We explore costly sequential search among finitely many risky options, and an outside option. Payoffs are the sum of a known and hidden random factor.

(a) We resolve a long open question about how riskier payoffs impact search duration: expected search time is higher for more dispersed idiosyncratic noise.

(b) Since options differ ex ante, we incorporate selection effects into search: Counterintuitively, with few options, the quitting chance falls if search costs rise; also, while stopping rates rise over time, earlier options are recalled more.

(c) We find that the stationary search model is a misleading benchmark: For as the number of options explodes, the recall chance is bounded away from zero if the known factor has a distribution without a thin tail (eg. exponential).

(d) A special case of our model captures web search engines that rank order options: We prove that the click through rate — the chance of initiating a search — is a poor quality measure since it falls in accuracy for expensive goods.

Keywords: sequential and nonstationary search, duration, logconcavity, dispersion, accuracy

JEL Classification: D81, D83

Suggested Citation

Choi, Michael and Smith, Lones, Optimal Sequential Search Among Alternatives (October 26, 2016). Available at SSRN: https://ssrn.com/abstract=2858097 or http://dx.doi.org/10.2139/ssrn.2858097

Michael Choi (Contact Author)

University of California, Irvine ( email )

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

Lones Smith

University of Wisconsin at Madison - Department of Economics ( email )

1180 Observatory Drive
Madison, WI 53706-1393
United States
608-263-3871 (Phone)
608-262-2033 (Fax)

HOME PAGE: http://www.lonessmith.com

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